It is more than just reading the file

Pandas is arguably the most popular data analysis and manipulation library. What I think makes Pandas widely-used is having a large number of powerful and versatile functions.

Pandas functions usually do a fine job with the default settings. However, they offer much more if you use the parameters efficiently. In this article, we will elaborate on the read_csv function to make the most of it.

The read_csv is one of the most commonly used Pandas functions. It creates a dataframe by reading data from a csv file. However, it is almost always executed with the default settings.

If you ever read through the documentation, you would notice the read_csv function has many parameters. These parameters add functionality and flexibility to the function.

For instance, if the csv file contains a column of dates, it will be stored in the dataframe with object data type. However, in order to use Pandas datetime functions under dt accessor, we need to have the dates with datetime data type. We can always convert the data type after reading the data. A more practical way is to handle this task while reading the data.

The parse_dates parameter accomplishes this task. Let’s do an example. I have a sample csv file with 3 columns.

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